Expression of the Frizzled receptors and their co-receptors in calcified human aortic valves
Bibliographic record
Abstract
The cellular mechanisms that induce calcific aortic stenosis are yet to be unraveled. Wnt signaling is increasingly being considered as a major player in the disease process. However, the presence of Wnt Frizzled (Fzd) receptors and co-receptors LRP5 and 6 in normal and diseased human aortic valves remains to be elucidated. Immunohistochemistry and quantitative polymerase chain reaction were used to determine Fzd receptor expression in normal and calcified human aortic valve tissue, as well as human aortic valve interstitial cells (HAVICs) isolated from calcified and normal human aortic valves. There was significantly higher mRNA expression of 4 out of the 10 Fzd receptors in calcified aortic valve tissues and 8 out of the 10 in HAVICs, and both LRP5/6 co-receptors in calcified aortic valves (P < 0.05). These results were confirmed by immunohistochemistry, which revealed abundant increase in immunoreactivity for Fzd3, 7, and 8, mainly in areas of lipid core and calcified nodules of diseased aortic valves. The findings of abundant expression of Fzd and LRP5/6 receptors in diseased aortic valves suggests a potential role for both canonical and noncanonical Wnt signaling in the pathogenesis of human aortic valve calcification. Future investigations aimed at targeting these molecules may provide potential therapies for aortic valve stenosis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".